Therefore, age does not effect the current sales number as any employee on staff with age as a non-factor is able to lead in the current sales figures.
4. The following is a regression model depicting the likelihood of buying a new car given total family income and the likelihood of buying tickets to a rock concert given age.
a.) Y^ = a^ + ss^X; Y^ = 3.5 + .7X, where Y^ = likelihood of buying a new car and X = total family income
b) Y^= a^ + ss^X; Y^= 3.5 - .4x, where Y^ = the likelihood of buying tickets to a rock concert and X = age.
The regression equation for a is a predictor of Y^ pronounced Y-hat suggests that for every unit increase in x, or family income, y increases by 4.2. So for every increase in total family income the likelihood of buying a new car increases by 4.2.
The regression equation for b is a predictor of Y^ and suggests that for every unit increase in x, or age, y increases by 3.9. For every unit increase in age, the likelihood of buying tickets to a rock concert increases by 3.9.
The ANOVA summary table is the result of a regression of sales on year of sales
Explained by regression 605, 370, 750 1-3.12 with the sum of squares, degrees of freedom, mean, and f-value shown respectively. Unexplained by the regression is 1,551,381,712 8 193,922, 714 and the total error is 9. The alpha value to test whether the relationship is statistically significant is alpha=.05 or 5% so the test is for 95% confidence or that the distribution is within 2?. Yes the test is significant at alpha .05 and does not need to test at alpha .01 however if one does not feel .05 is sufficient then testing at 3? is the next step.
6. A metropolitan economist attempts to predict the average total budget for retired couples in Phoenix based on the average of U.S. urban retired couples total budget. An r squared value of .7824 is the result of the analysis. This is to say that 78% of the data is explained by the dependant variable, or that 78% of the result is explained by the choice in dependant variable. A value above .8 would be a nice target but .78 is very close and therefore suggests that much of the data has been explained by using the mean of U.S. urban retired couples. The result would suggest that perhaps Phoenix may have to lower the predicted average of total budget required as the unexplained 21 and a fraction may imply...
A football teams season ticket sales, percentage of games won, and active alumni for the years 1992-2000 are given below:
The regression equation for sales and percentages of games won is
Y^ = 10528.34+3.48x
The regression equation for sales and number of active alumni is
Y^ = 8112.18+0.76x
8. No the testing of Liker-scaled items without first testing for their linear relationships brings in a measure of multi-collinearity and bias into the study. Additionally, the ethical nature of making decisions that may be use to discipline employees is critical to the operating ethically in a performance measurement environment. This may overstate the frequency of absenteeism or misinterpret the underlying assumptions and lead to a wrong decision or a misinformed decision.
Sheet1
Year Price Mile
Year 1-0.87016 0.95127
0 0.0551 0.0128
Corr Coef 0.9844181083
Price 0.87016 1-0.97309
0.0551 0-0.0053
Corr Coef 0.9972286364
Mile 0.95127 0.97309 1
0.0128 0.0053 0
Corr Coef 0.9946712315
Sheet1
Age Years of Service Current Sales
Age 1-0.68185 0.21652
0 0.0208 0.5225
0.6400633803
Years 0.68185 1-0.64499
of Service 0.0208 0-0.0321
0.4926283653
Current 0.21652 0.64499 1
Sales 0.5225 0.321 0
-0.3160124891
Sheet1
Year Ticket Sales Games won # of active alumni
1992 4995 40
1993 8599 54
1994 8479 55
1995 8419 58
1996 10253 63
1997 12457 75 6315
1998 13285 36 6860
1999 14177 27 8423
2000 15730 63 9000
a b c d correlation ab 0.9739950821
ac -0.2160452422
ad 0.9632597464
bc -0.1597845977
bd 0.9688058834
cd -0.8149159108
Sheet1
Regional Bus Line
Correlation of data matrix
Regional Bus Line Data
Cost of Gas Passenger/Mile Ratio
56.5-8.37
59.4-8.93
x y Correlation Coeff
63 9.15 0.9990066988
65.6-9.79 x =…
